The
West Nile Virus (WNV) is a mosquito-born flavivirus that causes
neurologic diseases such as encephalitis, meningitis, and acute
flaccid paralysis (Lim, Koraka, Osterhaus, & Martina, 2011).
Similar to other flaviviruses, WNV is an enveloped virus with a
single-stranded, positive sense, ∼11-kb RNA genome whose strains
are grouped into at least 7 genetic lineages. WNV was first isolated
in Uganda in 1937. Posteriorly, the first large outbreak of West Nile
neuroinvasive disease (WNND) was recorded in Romania in 1996, with
393 confirmed cases (Tsai, Popovici, Cernescu, Campbell, &
Nedelcu, 1998). Three years later, it became a global public
health concern after its introduction into North America, and
subsequently into Central and South America (Lanciotti et al.,
1999). Since then, major outbreaks of WNV fever and encephalitis
took place in all continents, apart from Antarctica, causing human
and animal deaths. Although its enzootic cycle is mainly maintained
between mosquitoes and birds, it can eventually infect horses,
humans, and other vertebrates (Hayes et al., 2005). Despite this
variety of hosts, studies on the host structure and its influence on
the spatiotemporal structure are still scarce. Since host genetic
factors have a significant influence on disease distribution
patterns, the overall purpose of this study is to assess the host
structure of the phylogenetic relationships of WNV in a
phylogeographic context, taking the spatiotemporal structure into
account.
Specific
Objectives
To
identify the lineages of each viral strain.
To
infer the main events of host-shift.
To
determine the transmission paths within spatiotemporal structure.
Methods
1.
Sequence Data: All the available sequences of complete genome
of WNV, with collection times, and geographic locations will be
retrieved from GenBank. In order to identify and delete recombinants,
clones, and duplicates from the data base, I used Uclust v1.2.22q
with 99 % of identity. Sequences of Ilheus virus (ILHV), Usutu virus
(USUV), and Japanese encephalitis virus (JEV) will be used as the
outgroup. Subsequently, all the WNV sequences will be aligned using
the algorithm of multiple sequence alignment, implemented in MUSCLE
v3.8.31 (Edgar, 2004).
2.
Evolutionary rates: From this alignment, I will obtain a
subset of 11 partitions, which correspond to the complete genome, and
the genes that constitute it (C,
prM/M, E, NS1, NS2A, NS2B, NS3,
NS4A, NS4B, and NS5). An exploration of evolution rates of every gene
will be done using Distance Rates (DistR) method (Bevan, Lang, &
Bryant, 2005) to get a first approach to the molecular evolution
of the genes, as one of the key determinants of the occurrence of
cross-species transmission (Longdon, Brockhurst, Russell, Welch, &
Jiggins, 2014; Vrancken et al., 2015).
3.
Lineages identification: The substitution model
will be selected using Akaike information criterion with JmodelTest2
(Darriba, Taboada, Doallo, & Posada, 2012). With this model,
a Maximum likelihood (ML) inference will be performed using ExaML
v3.0.X, with 20 searches and 100 bootstrap replicates, which are
considered as sufficient for large data sets (Kozlov, Aberer, &
Stamatakis, 2015). Every lineage will be assumed as a monophyletic
group as sugested by (MacKenzie & Williams, 2009), and all the
obtained clades will be revised taking previous studies into account.
4.
Phylodynamics:
In
order to evaluate every lineage independently, the data set will
be
down sampled. Thus, the tree topologies,
model parameters, evolutionary rates, MRCA, viral population size
variation over time will be co-estimated independently for the
resultant lineages, using with an uncorrelated log-normal relaxed
clock model (rationale given in (May, Davis, Tesh, & Barrett,
2011), and the MCMC method implemented in the BEAST package v1.8.2
(Drummond, Suchard, Xie, & Rambaut, 2012). Bayesian
skyline plot will
be
used as a coalescent prior during the estimation over time of the
change in effective
population size per generation, per year (Ne.g).
The MCMC analysis will
be
run twice
for
50
million generations, with
sampling every 10000.
MCMC convergence will
be measured by
estimating the effective sampling size (ESS), using Tracer software
version 1.5 (http://tree.bio.ed.ac.uk/software/tracer/).
Uncertainties
will be estimated as
95% high probability densities (95% HPD).
The
results for the two runs will be combined for final analysis and
Bayesian Factor (BF) support for host shift. Transition rates
supported by a BF > 3 will be considered as significant support
for a host shift between species. The
obtained topologies will
be summarized
in a maximum clade credibility (MCC) tree, and
annotated by the use of TreeAnnotator
(http://beast.bio.ed.ac.uk/treeannotator).
5.
Host-Shift
Events:
To
determine whether there is a stronger influence of cross species
transmission (CST) in the genetic divergence over within species
transmission, I will compute Genetic distances in PAUP* v.4.0b10
(http://paup.csit.fsu.edu/) using models of nucleotide substitution
specific to the lineages, and compare them with a cutoff value.
Subsequently, transmission of WNV will be quantified by Metropolis
Coupled Markov Chain Monte Carlo (MC3) coalescent simulation of
migration rates, implemented in the program Migrate-N v3.6 (Beerli
& Palczewski, 2010).
The
model of transmission (whether asymmetrical, bi-directional,
symmetrical, inter alia) will
be assessed, and
the
transmission web will be visualized
using
this software. In
order to estimate the potential of the strains to jump into new hosts
(sensu
(Frost
& Volz, 2010), or to predict viral emergence, I will estimate the per
capita
cross-species transmission rate Rij, and the effective reproductive
number of a pathogen Re.
6. Host
Phylogeny and the spatiotemporal Structure: Genetic
population predictors: Ne.g, Rij, and Re will
be plotted in function of time.
References
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doi:10.1111/2041-210X.12293
